Image diagnosis using medical image volume data (hereinafter referred to as the “volume data”), imaged by a medical imaging apparatus such as X-ray CT (X-ray Computed Tomography) or MRI (Magnetic Resonance Imaging: nuclear magnetic resonance imaging), has been using a plurality of medical image processing algorithms to visualize the volume data that have three-dimensional information and thereby produce images to be used as an aid for diagnosis.
Technical advancements in recent years have caused a significant increase in number of tomographic images (hereinafter referred to also as the “slice images”) to be captured at once by a medical imaging apparatus, and thus in size of volume data to be output as the imaging result. In displaying images with use of medical image processing algorithms, it is very difficult to automate all the process of displaying images when safety and accuracy is taken into account. This makes it inevitable for surgeons and engineers to check and modify the images displayed through automation. However, manually checking the images lays a heavy burden on the surgeons and engineers when the volume data has a huge size. Accordingly, there is a demand for a more convenient and adaptable technique of checking and modifying the images displayed through automation in order to lighten the burden of manually checking the images.
Region growing is a main medical image processing algorithm employed for extracting a target region from volume data. In the region growing, a point or points called seed points are set manually or automatically, then pixel values of neighboring pixels of the set seed points are referred to determine if the pixel values fit set conditions, and if they do, the initial region or regions are grown from the set seed point or points to include the neighboring pixels.
The region growing is sensitive to where the seed points should be set and depending on the set conditions for the region growing, the region may be excessively grown even to areas which should not be extracted. A correcting method to be used in such a case is a reverse region growing method disclosed in Patent Document 1. The reverse region growing method is a method of return the grown region to its previous state by setting seed points again in the excessive areas and thereby shrinking the grown region.
A technique disclosed in Patent Document 2 is a method of obtaining an anatomical measurement value of a structure as a result of automatic extraction and then judging whether the value falls within a standard range thereby to automatically judge an appropriateness of the automatic extraction result.
A technique disclosed in Patent Document 3 is a method comprising automatically dividing an image into a plurality of basic regions according to feature amounts of pixels of the image, automatically integrating the plurality of basic regions into an integrated region based on features of the basic regions, automatically judging a kind of the integrated region, and specifying a position at which to correct a result of the judgment on the kind in order to correct the kind of a basic region or the integrated region.